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Noise figure (NF) and noise factor (F) are figures of merit that indicate degradation of the signal-to-noise ratio (SNR) that is caused by components in a signal chain.These figures of merit are used to evaluate the performance of an amplifier or a radio receiver, with lower values indicating better performance.
Two-dimensional slice through 3D Perlin noise at z = 0. Perlin noise is a type of gradient noise developed by Ken Perlin in 1983. It has many uses, including but not limited to: procedurally generating terrain, applying pseudo-random changes to a variable, and assisting in the creation of image textures.
The noise floor limits the smallest measurement that can be taken with certainty since any measured amplitude can on average be no less than the noise floor. A common way to lower the noise floor in electronics systems is to cool the system to reduce thermal noise, when this is the major noise source. In special circumstances, the noise floor ...
Perlin noise is the earliest form of lattice noise, which has become very popular in computer graphics. Perlin Noise is not suited for simulation because it is not divergence-free. Noises based on lattices, such as simulation noise and Perlin noise, are often calculated at different frequencies and summed together to form band-limited fractal ...
A long list of noise measures have been defined to measure noise in signal processing: in absolute terms, relative to some standard noise level, or relative to the desired signal level. They include: Dynamic range, often defined by inherent noise level; Signal-to-noise ratio (SNR), ratio of noise power to signal power
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A noise-figure meter could automate that procedure as follows: A gated broadband noise source (such as an avalanche diode) drives the device under test. A measurement is made with the noise source on; another measurement with the noise source off. From those measurements and the characteristics of the noise source, the noise figure can be ...
For color images with three RGB values per pixel, the definition of PSNR is the same except that the MSE is the sum over all squared value differences (now for each color, i.e. three times as many differences as in a monochrome image) divided by image size and by three.